



import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a      :  List[str]			     =			logging.get_logger(__name__)

a      :  Union[str, Any]			     =			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

a      :  Union[str, Any]			     =			{
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

a      :  List[str]			     =			{
    'ctrl': 256,
}

a      :  Optional[Any]			     =			{
    'Pregnancy': 168_629,
    'Christianity': 7_675,
    'Explain': 106_423,
    'Fitness': 63_440,
    'Saving': 63_163,
    'Ask': 27_171,
    'Ass': 95_985,
    'Joke': 163_509,
    'Questions': 45_622,
    'Thoughts': 49_605,
    'Retail': 52_342,
    'Feminism': 164_338,
    'Writing': 11_992,
    'Atheism': 192_263,
    'Netflix': 48_616,
    'Computing': 39_639,
    'Opinion': 43_213,
    'Alone': 44_967,
    'Funny': 58_917,
    'Gaming': 40_358,
    'Human': 4_088,
    'India': 1_331,
    'Joker': 77_138,
    'Diet': 36_206,
    'Legal': 11_859,
    'Norman': 4_939,
    'Tip': 72_689,
    'Weight': 52_343,
    'Movies': 46_273,
    'Running': 23_425,
    'Science': 2_090,
    'Horror': 37_793,
    'Confession': 60_572,
    'Finance': 12_250,
    'Politics': 16_360,
    'Scary': 191_985,
    'Support': 12_654,
    'Technologies': 32_516,
    'Teenage': 66_160,
    'Event': 32_769,
    'Learned': 67_460,
    'Notion': 182_770,
    'Wikipedia': 37_583,
    'Books': 6_665,
    'Extract': 76_050,
    'Confessions': 102_701,
    'Conspiracy': 75_932,
    'Links': 63_674,
    'Narcissus': 150_425,
    'Relationship': 54_766,
    'Relationships': 134_796,
    'Reviews': 41_671,
    'News': 4_256,
    'Translation': 26_820,
    'multilingual': 128_406,
}







def  lowerCAmelCase_	(lowerCAmelCase__:		str  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     set()
			UpperCAmelCase_:		str							      =     word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)  )
						UpperCAmelCase_:		str							      =     char

			UpperCAmelCase_:		List[str]							      =     set(lowerCAmelCase__  )
			return pairs







class 				_a				(   _lowerCAmelCase					):
			A      		=	VOCAB_FILES_NAMES
			A      		=	PRETRAINED_VOCAB_FILES_MAP
			A      		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A      		=	CONTROL_CODES

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_="<unk>",   **SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						super().__init__(unk_token=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

						with open(SCREAMING_SNAKE_CASE_,   encoding="""utf-8"""					) as vocab_handle:
									UpperCAmelCase_:		Optional[int]							      =     json.load(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     {v: k for k, v in self.encoder.items()}
						with open(SCREAMING_SNAKE_CASE_,   encoding="""utf-8"""					) as merges_handle:
									UpperCAmelCase_:		List[str]							      =     merges_handle.read().split("""\n"""					)[1:-1]
						UpperCAmelCase_:		List[str]							      =     [tuple(merge.split()					) for merge in merges]
						UpperCAmelCase_:		Dict							      =     dict(zip(SCREAMING_SNAKE_CASE_,   range(len(SCREAMING_SNAKE_CASE_					)					)					)					)
						UpperCAmelCase_:		List[Any]							      =     {}

			@property
			def      __snake_case       (self					)       ->   Union[str, Any]:
						return len(self.encoder					)

			def      __snake_case       (self					)       ->   Optional[int]:
						return dict(self.encoder,   **self.added_tokens_encoder					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						if token in self.cache:
									return self.cache[token]
						UpperCAmelCase_:		int							      =     tuple(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     tuple(list(word[:-1]					) + [word[-1] + """</w>"""]					)
						UpperCAmelCase_:		str							      =     get_pairs(SCREAMING_SNAKE_CASE_					)

						if not pairs:
									return token

						while True:
									UpperCAmelCase_:		Union[str, Any]							      =     min(SCREAMING_SNAKE_CASE_,   key=lambda SCREAMING_SNAKE_CASE_					: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_,   float("""inf"""					)					)					)
									if bigram not in self.bpe_ranks:
												break
									UpperCAmelCase_      ,							UpperCAmelCase_:		List[Any]							      =     bigram
									UpperCAmelCase_:		int							      =     []
									UpperCAmelCase_:		int							      =     0
									while i < len(SCREAMING_SNAKE_CASE_					):
												try:
															UpperCAmelCase_:		Tuple							      =     word.index(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
												except ValueError:
															new_word.extend(word[i:]					)
															break
												else:
															new_word.extend(word[i:j]					)
															UpperCAmelCase_:		Tuple							      =     j

												if word[i] == first and i < len(SCREAMING_SNAKE_CASE_					) - 1 and word[i + 1] == second:
															new_word.append(first + second					)
															i += 2
												else:
															new_word.append(word[i]					)
															i += 1
									UpperCAmelCase_:		List[str]							      =     tuple(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Tuple							      =     new_word
									if len(SCREAMING_SNAKE_CASE_					) == 1:
												break
									else:
												UpperCAmelCase_:		List[Any]							      =     get_pairs(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     """@@ """.join(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     word[:-4]
						UpperCAmelCase_:		Tuple							      =     word
						return word

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Dict							      =     []

						UpperCAmelCase_:		Optional[int]							      =     re.findall(R"""\S+\n?""",   SCREAMING_SNAKE_CASE_					)

						for token in words:
									split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_					).split(""" """					)					)					)
						return split_tokens

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						return self.encoder.get(SCREAMING_SNAKE_CASE_,   self.encoder.get(self.unk_token					)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Any:
						return self.decoder.get(SCREAMING_SNAKE_CASE_,   self.unk_token					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Tuple:
						UpperCAmelCase_:		str							      =     """ """.join(SCREAMING_SNAKE_CASE_					).replace("""@@ """,   """"""					).strip()
						return out_string

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None					)       ->   Tuple[str]:
						if not os.path.isdir(SCREAMING_SNAKE_CASE_					):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
									return
						UpperCAmelCase_:		List[str]							      =     os.path.join(
						    SCREAMING_SNAKE_CASE_,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
						UpperCAmelCase_:		List[str]							      =     os.path.join(
						    SCREAMING_SNAKE_CASE_,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]					)

						with open(SCREAMING_SNAKE_CASE_,   """w""",   encoding="""utf-8"""					) as f:
									f.write(json.dumps(self.encoder,   indent=2,   sort_keys=SCREAMING_SNAKE_CASE_,   ensure_ascii=SCREAMING_SNAKE_CASE_					) + """\n"""					)

						UpperCAmelCase_:		Tuple							      =     0
						with open(SCREAMING_SNAKE_CASE_,   """w""",   encoding="""utf-8"""					) as writer:
									writer.write("""#version: 0.2\n"""					)
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),   key=lambda SCREAMING_SNAKE_CASE_					: kv[1]					):
												if index != token_index:
															logger.warning(
															    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
															    """ Please check that the tokenizer is not corrupted!"""					)
															UpperCAmelCase_:		Union[str, Any]							      =     token_index
												writer.write(""" """.join(SCREAMING_SNAKE_CASE_					) + """\n"""					)
												index += 1

						return vocab_file, merge_file

			# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
			#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
			#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
			#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
			#     return ''.join(tokens_generated_so_far)











#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






